Dynamic Susceptibility Contrast Perfusion-weighted Imaging Research Articles

Comparison of normalized cerebral blood flow between different post-processing methods of dynamic susceptibility contrast perfusion-weighted imaging and arterial spin labeling in gliomas with different grading.

Two post-processing methods of dynamic susceptibility contrast perfusion-weighted imaging (DSC-PWI), arterial input function (AIF) and gamma-variate fitting (GVF), can both derive cerebral blood flow (CBF). Moreover, AIF can provide T2* and T1 leakage indicators. This study aimed to compare the consistency of normalized CBF between different post-processing methods of DSC-PWI and arterial spin labeling (ASL) in gliomas, and take the quantitative metrics percentage of signal recovery (PSR) as a reference to verify the value of T2* and T1 leakage indicators in characterizing leakage effect and evaluating the grading of gliomas. From 1 January 2020 to 15 December 2023, 56 consecutive inpatients were retrospectively enrolled, comprising 24 patients with low-grade glioma (LGG) and 32 patients with high-grade glioma (HGG). The normalized CBF was derived from AIF, GVF and ASL. The T2* and T1 leakage indicators of AIF were graded by 4-point scale. The consistency and difference of normalized CBF between DSC-PWI and ASL were tested by linear correlation/regression analysis, Bland-Altman plots, and Student's t-test. The correlation between the difference of point for both leakage indicators and PSR was tested by Spearman correlation analysis, then inter-group difference of PSR was compared by t-test. The intra-group and inter-group differences of point for T2* and T1 leakage indicators were compared by χ2/Fisher's exact test. The normalized CBF derived from AIF and GVF were correlated with ASL in both groups (all r>0.7, all P<0.001), and linear regressions were not significantly different in each group (all P>0.05). The difference of normalized CBF between ASL and AIF in the HGG group was larger than that in the LGG group (P=0.017); however, the difference of normalized CBF between ASL and GVF was not significant (P=0.085). The strong correlation between the difference of point for both leakage indicators and PSR was verified (r=-0.739, P<0.0001), and the HGG group was shown to have higher PSR compared with the LGG group (t=2.043, P=0.04). The comparison of intra-group and inter-group differences in T2* leakage and T1 indicators showed that the HGG group was more prone to T1 leakage than the LGG group (P<0.05), and weight of T1 leakage was greater than that of T2* leakage (χ2=11.28, P=0.004). The normalized CBF derived from DSC-PWI has good consistency with ASL in gliomas, regardless of post-processing methods. GVF can provide less bias of normalized CBF between HGG and LGG groups. However, T2* and T1 leakage indicators of AIF can be utilized as a surrogate of PSR to characterize both leakage effects and evaluate glioma grading.

Advancing ischemic stroke diagnosis and clinical outcome prediction using improved ensemble techniques in DSC-PWI radiomics

Ischemic stroke is a leading global cause of death and disability and is expected to rise in the future. The present diagnostic techniques, like CT and MRI, have some limitations in distinguishing acute from chronic ischemia and in early ischemia detection. This study investigates the function of ensemble models based on the dynamic radiomics features (DRF) from the dynamic susceptibility contrast perfusion-weighted imaging (DSC-PWI) ischemic stroke diagnosis, neurological impairment assessment, and modified Rankin Scale (mRS) outcome prediction). DRF is extracted from the 3D images, features are selected, and dimensionality is reduced. After that, ensemble models are applied. Two model structures were developed: a voting classifier with 6 bagging classifiers and a stacking classifier based on 4 bagging classifiers. The ensemble models were evaluated on three core tasks. The Stacking_ens_LR model performed best for ischemic stroke detection, the LR Bagging model for NIH Stroke Scale (NIHSS) prediction, and the NB Bagging model for outcome prediction. These outcomes illustrate the strength of ensemble models. The work showcases the role of ensemble models and DRF in the stroke management process.

Leveraging Ensemble Models and Follow-up Data for Accurate Prediction of mRS Scores from Radiomic Features of DSC-PWI Images.

Predicting long-term clinical outcomes based on the early DSC PWI MRI scan is valuable for prognostication, resource management, clinical trials, and patient expectations. Current methods require subjective decisions about which imaging features to assess and may require time-consuming postprocessing. This study's goal was to predict multilabel 90-day modified Rankin Scale (mRS) score in acute ischemic stroke patients by combining ensemble models and different configurations of radiomic features generated from Dynamic susceptibility contrast perfusion-weighted imaging. In Follow-up studies, a total of 70 acute ischemic stroke (AIS) patients underwent magnetic resonance imaging within 24hours poststroke and had a follow-up scan. In the single study, 150 DSC PWI Image scans for AIS patients. The DRF are extracted from DSC-PWI Scans. Then Lasso algorithm is applied for feature selection, then new features are generated from initial and follow-up scans. Then we applied different ensemble models to classify between three classes normal outcome (0, 1 mRS score), moderate outcome (2,3,4 mRS score), and severe outcome (5,6 mRS score). ANOVA and post-hoc Tukey HSD tests confirmed significant differences in model style performance across various studies and classification techniques. Stacking models consistently on average outperformed others, achieving an Accuracy of 0.68 ± 0.15, Precision of 0.68 ± 0.17, Recall of 0.65 ± 0.14, and F1 score of 0.63 ± 0.15 in the follow-up time study. Techniques like Bo_Smote showed significantly higher recall and F1 scores, highlighting their robustness and effectiveness in handling imbalanced data. Ensemble models, particularly Bagging and Stacking, demonstrated superior performance, achieving nearly 0.93 in Accuracy, 0.95 in Precision, 0.94 in Recall, and 0.94 in F1 metrics in follow-up conditions, significantly outperforming single models. Ensemble models based on radiomics generated from combining Initial and follow-up scans can be used to predict multilabel 90-day stroke outcomes with reduced subjectivity and user burden.

Ischemic perfusion radiomics: assessing neurological impairment in acute ischemic stroke.

Accurate neurological impairment assessment is crucial for the clinical treatment and prognosis of patients with acute ischemic stroke (AIS). However, the original perfusion parameters lack the deep information for characterizing neurological impairment, leading to difficulty in accurate assessment. Given the advantages of radiomics technology in feature representation, this technology should provide more information for characterizing neurological impairment. Therefore, with its rigorous methodology, this study offers practical implications for clinical diagnosis by exploring the role of ischemic perfusion radiomics features in assessing the degree of neurological impairment. This study employs a meticulous methodology, starting with generating perfusion parameter maps through Dynamic Susceptibility Contrast-Perfusion Weighted Imaging (DSC-PWI) and determining ischemic regions based on these maps and a set threshold. Radiomics features are then extracted from the ischemic regions, and the t-test and least absolute shrinkage and selection operator (Lasso) algorithms are used to select the relevant features. Finally, the selected radiomics features and machine learning techniques are used to assess the degree of neurological impairment in AIS patients. The results show that the proposed method outperforms the original perfusion parameters, radiomics features of the infarct and hypoxic regions, and their combinations, achieving an accuracy of 0.926, sensitivity of 0.923, specificity of 0.929, PPV of 0.923, NPV of 0.929, and AUC of 0.923, respectively. The proposed method effectively assesses the degree of neurological impairment in AIS patients, providing an objective auxiliary assessment tool for clinical diagnosis.

Dynamic processes of positive/negative immune-vascular crosstalk loop via functional MRI-guided monitor

Immunotherapy holds significant promise to completely eliminate tumor, yet lack of timely evaluation of immune responses, which challenged by inherent lagging and pseudoprogression during immunotherapy. Immune-vascular crosstalk (IVC), an enhancement ring for the mutual regulation linking tumor vascular normalization with immune remodeling forms, emerging as a systematic indicator for immunotherapy outcomes, necessitates timely, non-invasive, and early detection of its status to enhance clinical benefits. Herein, positive and negative loop models of tumor IVC were established using different degree of photothermal therapy (PTT) based on NaBiF4:Gd@PDA@PEG nanoprobes. Multi-functional magnetic resonance imaging, including fast spoiled gradient-recalled (FSPGR), time-resolved imaging of contrast kinetics (TRICKs) and dynamic susceptibility contrast perfusion weighted imaging (DSC-PWI), was applied to dynamically reflect normalized/ablated tumor vasculature, well-matched increased/decreased immune response corresponding to different status of IVC, confirmed by pathology results. This study proposes a novel biological method to noninvasively detect the positive/negative IVC loop, paving the visualized way for immunotherapy assessed benefits, which is expected to be extended to clinical translation.

Ischemic stroke outcome prediction with diversity features from whole brain tissue using deep learning network.

This study proposed an outcome prediction method to improve the accuracy and efficacy of ischemic stroke outcome prediction based on the diversity of whole brain features, without using basic information about patients and image features in lesions. In this study, we directly extracted dynamic radiomics features (DRFs) from dynamic susceptibility contrast perfusion-weighted imaging (DSC-PWI) and further extracted static radiomics features (SRFs) and static encoding features (SEFs) from the minimum intensity projection (MinIP) map, which was generated from the time dimension of DSC-PWI images. After selecting whole brain features Ffuse from the combinations of DRFs, SRFs, and SEFs by the Lasso algorithm, various machine and deep learning models were used to evaluate the role of Ffuse in predicting stroke outcomes. The experimental results show that the feature Ffuse generated from DRFs, SRFs, and SEFs (Resnet 18) outperformed other single and combination features and achieved the best mean score of 0.971 both on machine learning models and deep learning models and the 95% CI were (0.703, 0.877) and (0.92, 0.983), respectively. Besides, the deep learning models generally performed better than the machine learning models. The method used in our study can achieve an accurate assessment of stroke outcomes without segmentation of ischemic lesions, which is of great significance for rapid, efficient, and accurate clinical stroke treatment.

Predicting glioblastoma recurrence using multiparametric MR imaging of non-enhancing peritumoral regions at baseline

BackgroundTo assess the feasibility of multiparametric magnetic resonance imaging in predicting tumor recurrence in nonenhancing peritumoral regions in patients with glioblastoma at baseline. MethodsFifty-eight patients with recurrent glioblastoma underwent multiparametric magnetic resonance imaging, including T2-weighted fluid-attenuated inversion recovery, diffusion-weighted imaging, and dynamic susceptibility contrast perfusion-weighted imaging. Nonenhancing peritumoral regions with glioblastoma recurrence were identified by coregistering preoperative and post-recurrent magnetic resonance images. Regions of interest were placed in nonenhancing peritumoral regions with and without tumor recurrence to calculate the apparent diffusion coefficient value, and relative ratios of T2-weighted fluid-attenuated inversion recovery signal intensity, apparent diffusion coefficient, and cerebral blood volume values. ResultsSignificant lower relative T2-weighted fluid-attenuated inversion recovery signal intensity, apparent diffusion coefficient, and relative apparent diffusion coefficient but higher relative cerebral blood volume values were found in the nonenhancing peritumoral regions with tumor recurrence than without recurrence (all P < 0.05). The threshold values ≥ 0.89 for relative cerebral blood volume provide the optimal performance for predicting the nonenhancing peritumoral regions with future tumor recurrence, with the sensitivity, specificity, and accuracy of 84.7%, 83.6%, and 85.8%, respectively. The combination of relative T2-weighted fluid-attenuated inversion recovery signal intensity, apparent diffusion coefficient, and relative cerebral blood volume can provide better predictive performance than relative cerebral blood volume (P = 0.015). ConclusionThe combined use of T2-weighted fluid-attenuated inversion recovery, diffusion-weighted imaging, and dynamic susceptibility contrast perfusion-weighted imaging can effectively estimate the risk of future tumor recurrence at baseline.

Comparison of computed tomography perfusion and magnetic resonance dynamic susceptibility contrast perfusion-weighted imaging in canine brain.

Brain perfusion allows for the evaluation of cerebral hemodynamics, particularly in brain infarcts and tumors. Computed tomography (CT) perfusion (CTP) provides reliable data; however, it has a limited scan field of view and radiation exposure. Magnetic resonance (MR) perfusion provides detailed imaging of small structures and a wide scan field of view. However, no study has compared CTP and MR perfusion and assessed the correlation between the perfusion parameters measured using CTP and MR perfusion. The aim of the present study was to assess the correlation and agreement of the cerebral perfusion derived from dynamic susceptibility contrast (DSC)-MRI and CTP in dogs. In this crossover design study, the cerebral blood volume (CBV), cerebral blood flow (CBF), mean transit time, and time to peak were measured in the temporal cerebral cortex, caudate nucleus, thalamus, piriform lobe, and hippocampus using CTP and DSC-MRI in six healthy beagle dogs and a dog with a pituitary tumor. On the color map of healthy beagles, blood vessels and the perivascular brain parenchyma appeared as red-green, indicating high perfusion, and the areas distant from the vessels appeared as green-blue, indicating low perfusion levels in CTP and DSC-MRI. CTP parameters were highest in the piriform lobe (CBF = 121.11 ± 12.78 mL/100 g/min and CBV = 8.70 ± 2.04 mL/100 g) and lowest in the thalamus (CBF = 63.75 ± 25.24 mL/100 g/min and CBV = 4.02 ± 0.55 mL/100 g). DSC-MRI parameters were also highest in the piriform lobe (CBF = 102.31 ± 14.73 mL/100 g/min and CBV = 3.17 ± 1.23 mL/100 g) and lowest in the thalamus (CBF = 37.73 ± 25.11 mL/100 g/min and CBV = 0.81 ± 0.44 mL/100 g) although there was no statistical correlation in the quantitative perfusion parameters between CTP and DSC-MRI. In a dog with a pituitary tumor, the color map of the tumor appeared as a red scale, indicating high perfusion and higher CBF and CBV on CTP (149 mL/100 g and 20 mL/100 g/min) and on DSC-MRI (116.3 mL/100 g and 15.32 mL/100 g/min) compared to those measured in healthy dogs. These findings indicate that DSC-MRI and CTP maps exhibit comparability and interchangeability in the assessment of canine brain perfusion.

Can Perfusion-Based Brain Tissue Oxygenation MRI Support the Understanding of Cerebral Abscesses In Vivo?

The clinical condition of a brain abscess is a potentially life-threatening disease. The combination of MRI-based imaging, surgical therapy and microbiological analysis is critical for the treatment and convalescence of the individual patient. The aim of this study was to evaluate brain tissue oxygenation measured with dynamic susceptibility contrast perfusion weighted imaging (DSC-PWI) in patients with brain abscess and its potential benefit for a better understanding of the environment in and around brain abscesses. Using a local database, 34 patients (with 45 abscesses) with brain abscesses treated between January 2013 and March 2021 were retrospectively included in this study. DSC-PWI imaging and microbiological work-up were key inclusion criteria. These data were analysed regarding a correlation between DSC-PWI and microbiological result by quantifying brain tissue oxygenation in the abscess itself, the abscess capsula and the surrounding oedema and by using six different parameters (CBF, CBV, CMRO2, COV, CTH and OEF). Relative cerebral blood flow (0.335 [0.18-0.613] vs. 0.81 [0.49-1.08], p = 0.015), relative cerebral blood volume (0.44 [0.203-0.72] vs. 0.87 [0.67-1.2], p = 0.018) and regional cerebral metabolic rate for oxygen (0.37 [0.208-0.695] vs. 0.82 [0.55-1.19], p = 0.022) were significantly lower in the oedema around abscesses without microbiological evidence of a specific bacteria in comparison with microbiological positive lesions. The results of this study indicate a relationship between brain tissue oxygenation status in DSC-PWI and microbiological/inflammatory status. These results may help to better understand the in vivo environment of brain abscesses and support future therapeutic decisions.

Impaired T1 mapping and Tmax during the first 7 days after ischemic stroke. A retrospective observational study

ObjectiveTo measure the relative T1 (rT1) value in different hypo-perfused regions after ischemic stroke using T1 mapping derived by Strategically Acquired Gradient Echo (STAGE) and assess its relationship with onset time and severity of ischemia. Materials and MethodsSixty-three patients with acute anterior circulation ischemic stroke from 2017 to 2022 who underwent STAGE, diffusion weighted imaging (DWI) and dynamic susceptibility contrast perfusion weighted imaging (DSC-PWI) within 7 days were retrospectively enrolled. The areas with reduced diffusion and hypo-perfusion were segmented based on apparent diffusion coefficient (ADC) value < 0.62 × 10−3mm2/s and time-to-maximum (Tmax) thresholds (4, 6, 8, and 10 seconds). We measured the T1 value in the diffusion reduced and every 2 s Tmax strata regions and calculated rT1 (T1ipsi/T1contra) to explore the relationship between rT1 value, Tmax, and onset time. ResultsrT1 value was increased in diffusion reduced (1.42) and hypo-perfused regions (1.02, 1.06, 1.12, 1.27, Tmax 4–6 s, 6–8 s, 8–10 s, > 10 s, respectively; all different from 1, P < 0.001). rT1 value was positively correlated with Tmax (rs = 0.61, P < 0.001) and onset time in area with reduced diffusion (rs = 0.39, P = 0.014). ConclusionsIncreased rT1 value in different hypo-perfused brain regions using T1 mapping derived by STAGE may reflect the edema; it was associated with the severity of Tmax and showed a weak correlation with the onset time in diffusion reduced areas.

Deep learning based on dynamic susceptibility contrast MR imaging for prediction of local progression in adult-type diffuse glioma (grade 4)

Adult-type diffuse glioma (grade 4) has infiltrating nature, and therefore local progression is likely to occur within surrounding non-enhancing T2 hyperintense areas even after gross total resection of contrast-enhancing lesions. Cerebral blood volume (CBV) obtained from dynamic susceptibility contrast perfusion-weighted imaging (DSC-PWI) is a parameter that is well-known to be a surrogate marker of both histologic and angiographic vascularity in tumors. We built two nnU-Net deep learning models for prediction of early local progression in adult-type diffuse glioma (grade 4), one using conventional MRI alone and one using multiparametric MRI, including conventional MRI and DSC-PWI. Local progression areas were annotated in a non-enhancing T2 hyperintense lesion on preoperative T2 FLAIR images, using the follow-up contrast-enhanced (CE) T1-weighted (T1W) images as the reference standard. The sensitivity was doubled with the addition of nCBV (80% vs. 40%, P = 0.02) while the specificity was decreased nonsignificantly (29% vs. 48%, P = 0.39), suggesting that fewer cases of early local progression would be missed with the addition of nCBV. While the diagnostic performance of CBV model is still poor and needs improving, the multiparametric deep learning model, which presumably learned from the subtle difference in vascularity between early local progression and non-progression voxels within perilesional T2 hyperintensity, may facilitate risk-adapted radiotherapy planning in adult-type diffuse glioma (grade 4) patients.

Dynamic susceptibility contrast perfusion-weighted and diffusion-weighted magnetic resonance imaging findings in pilocytic astrocytoma and H3.3 and H3.1 variant diffuse midline glioma, H3K27-altered.

This study compared the dynamic susceptibility contrast (DSC) magnetic resonance imaging parameters and apparent diffusion coefficient (ADC) between pilocytic astrocytoma (PA) and diffuse midline glioma, H3K27-altered (DMG) variants. The normalized relative cerebral blood volume (nrCBV), normalized relative flow (nrCBF), percentile signal recovery (PSR), and normalized mean ADC (nADCmean) of 23 patients with midline PAs (median age, 13 years [range, 1-71 years]; 13 female patients) and 40 patients with DMG (8.5 years [1-35 years]; 19 female patients), including 35 patients with H3.3- and five patients with H3.1-mutant tumors, treated between January 2016 and May 2022 were statistically compared. DMG had a significantly lower nADCmean (median: 1.48 vs. 1.96; p = 0.00075) and lower PSR (0.97 vs. 1.23, p = 0.13) but higher nrCBV and nrCBF (1.66 vs. 1.17, p = 0.058, respectively, and 1.87 vs. 1.19, p = 0.028, respectively) than PA. The H3.3 variant had a lower nADCmean than the H3.1 variant (1.46 vs. 1.80, p = 0.10). DMG had lower ADC and PSR and higher rCBV and rCBF than PA. The H3.3 variant had a lower ADC than the H3.1 variant. Recognizing the differences and similarities in the DSC parameters and ADC between these tumors may help presurgical diagnosis.

MR dynamic-susceptibility-contrast perfusion metrics in the presurgical discrimination of adult solitary intra-axial cerebellar tumors.

Adult solitary intra-axial cerebellar tumors are uncommon. Their presurgical differentiation based on neuroimaging is crucial, since management differs substantially. Comprehensive full assessment of MR dynamic-susceptibility-contrast perfusion-weighted imaging (DSC-PWI) may reveal key differences between entities. This study aims to provide new insights on perfusion patterns of these tumors and to explore the potential of DSC-PWI in their presurgical discrimination. Adult patients with a solitary cerebellar tumor on presurgical MR and confirmed histological diagnosis of metastasis, medulloblastoma, hemangioblastoma, or pilocytic astrocytoma were retrospectively retrieved (2008-2023). Volumetric segmentation of tumors and normal-appearing white matter (for normalization) was semi-automatically performed on CE-T1WI and coregistered with DSC-PWI. Mean normalized values per patient tumor-mask of relative cerebral blood volume (rCBV), percentage of signal recovery (PSR), peak height (PH), and normalized time-intensity curves (nTIC) were extracted. Statistical comparisons were done. Then, the dataset was split into training (75%) and test (25%) cohorts and a classifier was created considering nTIC, rCBV, PSR, and PH in the model. Sixty-eight patients (31 metastases, 13 medulloblastomas, 13 hemangioblastomas, and 11 pilocytic astrocytomas) were included. Relevant differences between tumor types' nTICs were demonstrated. Hemangioblastoma showed the highest rCBV and PH, pilocytic astrocytoma the highest PSR. All parameters showed significant differences on the Kruskal-Wallis tests (p < 0.001). The classifier yielded an accuracy of 98% (47/48) in the training and 85% (17/20) in the test sets. Intra-axial cerebellar tumors in adults have singular and significantly different DSC-PWI signatures. The combination of perfusion metrics through data-analysis rendered excellent accuracies in discriminating these entities. In this study, the authors constructed a classifier for the non-invasive imaging presurgical diagnosis of adult intra-axial cerebellar tumors. The resultant tool can be a support for decision-making in the clinical practice and enables optimal personalized patient management. • Adult intra-axial cerebellar tumors exhibit specific, singular, and statistically significant different MR dynamic-susceptibility-contrast perfusion-weighted imaging (DSC-PWI) signatures. • Data-analysis, applied to MR DSC-PWI, could provide added value in the presurgical diagnosis of solitary cerebellar metastasis, medulloblastoma, hemangioblastoma, and pilocytic astrocytoma. • A classifier based on DSC-PWI metrics yields excellent accuracy rates and could be used as a support tool for radiologic diagnosis with clinician-friendly displays.

Improved performance of non-preloaded and high flip-angle dynamic susceptibility contrast perfusion-weighted imaging sequences in the presurgical differentiation of brain lymphoma and glioblastoma.

This study aimed to compare the accuracy of relative cerebral blood volume (rCBV) and percentage signal recovery (PSR) obtained from high flip-angle dynamic susceptibility contrast perfusion-weighted imaging (DSC-PWI) sequences with and without contrast agent (CA) preload for presurgical discrimination of brain glioblastoma and lymphoma. Consecutive 336 patients (glioblastoma, 236; PCNSL, 100) were included. All the patients underwent DSC-PWI on 3.0-T magnetic resonance units before surgery. The rCBV and PSR with preloaded and non-preloaded CA were measured. The means of the continuous variables were compared using Welch's t-test. The diagnostic accuracies of the individual parameters were compared using the receiver operating characteristic curve analysis. The rCBV was higher with preloaded CA than with non-preloaded CA (glioblastoma, 10.20 vs. 8.90, p = 0.020; PCNSL, 3.88 vs. 3.27, p = 0.020). The PSR was lower with preloaded CA than with non-preloaded CA (glioblastoma, 0.59 vs. 0.90; PCNSL, 0.70 vs. 1.63; all p < 0.001). Regarding the differentiation of glioblastoma and PCNSL, the AUC of rCBV with preloaded CA was indistinguishable from that of non-preloaded CA (0.940 vs. 0.949, p = 0.703), whereas the area under the curve of PSR with preloaded CA was lower than non-preloaded CA (0.529 vs. 0.884, p < 0.001). With preloaded CA, diagnostic performance in differentiating glioblastoma and PCNSL did not improve for rCBV and it was decreased for PSR. Therefore, high flip-angle non-preload DSC-PWI sequences offer excellent accuracy and may be of choice sequence for presurgical discrimination of brain lymphoma and glioblastoma. High flip-angle DSC-PWI using non-preloaded CA may be an excellent diagnostic method for distinguishing glioblastoma from PCNSL. • Differentiating primary central nervous system lymphoma and glioblastoma accurately is critical for their management. • DSC-PWI sequences optimised for the most accurate CBV calculations may not be the optimal sequences for presurgical brain tumour diagnosis as they could be masquerading leakage phenomena that may provide interesting information in terms of differential diagnosis. • High flip-angle non-preloaded DSC-PWI sequences render the best accuracy in the presurgical differentiation of brain lymphoma and glioblastoma.

Taming Glioblastoma in "Real Time": Integrating Multimodal Advanced Neuroimaging/AI Tools Towards Creating a Robust and Therapy Agnostic Model for Response Assessment in Neuro-Oncology.

The highly aggressive nature of glioblastoma carries a dismal prognosis despite aggressive multimodal therapy. Alternative treatment regimens, such as immunotherapies, are known to intensify the inflammatory response in the treatment field. Follow-up imaging in these scenarios often mimics disease progression on conventional MRI, making accurate evaluation extremely challenging. To this end, revised criteria for assessment of treatment response in high-grade gliomas were successfully proposed by the RANO Working Group to distinguish pseudoprogression from true progression, with intrinsic constraints related to the postcontrast T1-weighted MRI sequence. To address these existing limitations, our group proposes a more objective and quantifiable "treatment agnostic" model, integrating into the RANO criteria advanced multimodal neuroimaging techniques, such as diffusion tensor imaging (DTI), dynamic susceptibility contrast-perfusion weighted imaging (DSC-PWI), dynamic contrast enhanced (DCE)-MRI, MR spectroscopy, and amino acid-based positron emission tomography (PET) imaging tracers, along with artificial intelligence (AI) tools (radiomics, radiogenomics, and radiopathomics) and molecular information to address this complex issue of treatment-related changes versus tumor progression in "real-time", particularly in the early posttreatment window. Our perspective delineates the potential of incorporating multimodal neuroimaging techniques to improve consistency and automation for the assessment of early treatment response in neuro-oncology.

Evaluation of the Value of Perfusion-Weighted Magnetic Resonance Imaging in the Differential Diagnosis of Sellar and Parasellar Tumors.

The purpose of this study was to assess the value of perfusion-weighted imaging (PWI) in the differential diagnosis of sellar and parasellar tumors, as an additional sequence in the magnetic resonance imaging (MRI) protocol. Analysis was based on a substantial group of subjects and included 124 brain and pituitary MRI examinations with a dynamic susceptibility contrast (DSC) PWI sequence. The following perfusion parameters were determined for the tumors: relative cerebral blood volume (rCBV), relative peak height (rPH) and relative percentage of signal intensity recovery (rPSR). To ensure greater repeatability, each of the aforementioned parameters was calculated as: arithmetic mean of the values of the whole tumor, arithmetic mean of the maximum values on each axial slice within the tumor and maximum values derived from the whole tumor. In our study, we established that meningiomas compared to both non-functional and hormone-secreting pituitary adenomas (pituitary neuroendocrine tumors-PitNET) had significantly higher values of rCBV with cut-off points set at 3.45 and 3.54, respectively (mean rCBV). Additionally, meningiomas presented significantly higher maximum and mean maximum rPH values compared to adenomas. DSC PWI imaging adds significant value to conventional MRI examinations and can be helpful in differentiating equivocal pituitary tumors.

Radiomic features from dynamic susceptibility contrast perfusion-weighted imaging improve the three-class prediction of molecular subtypes in patients with adult diffuse gliomas.

To investigate whether radiomic features extracted from dynamic susceptibility contrast perfusion-weighted imaging (DSC-PWI) can improve the prediction of the molecular subtypes of adultdiffuse gliomas, and to further develop and validate a multimodal radiomic model by integrating radiomic features from conventional and perfusion MRI. We extracted 1197 radiomic features from each sequence of conventional MRI and DSC-PWI, respectively. The Boruta algorithm was used for feature selection and combination, and a three-class random forest method was applied to construct the models. We also constructed a combined model by integrating radiomic features and clinical metrics. The models' diagnostic performance for discriminating the molecular subtypes (IDH wild type [IDHwt], IDH mutant and 1p/19q-noncodeleted [IDHmut-noncodel], and IDH mutant and 1p/19q-codeleted [IDHmut-codel]) was compared using AUCs in the validation set. We included 272 patients (training set, n = 166; validation set, n = 106) with grade II-IV gliomas (mean age, 48.7years; range, 19-77years). The proportions of the molecular subtypes were 66.2% IDHwt, 15.1% IDHmut-noncodel, and 18.8% IDHmut-codel. Nineteen radiomic features (13 from conventional MRI and 6 from DSC-PWI) were selected to build the multimodal radiomic model. In the validation set, the multimodal radiomic model showed better performance than the conventional radiomic model did in predicting the IDHwt and IDHmut-codel subtypes, which was comparable to the conventional radiomic model in predicting the IDHmut-noncodel subtype. The multimodal radiomic model yielded similar performance as the combined model in predicting the three molecular subtypes. Adding DSC-PWI to conventional MRI can improve molecular subtype prediction in patients with diffuse gliomas. • The multimodal radiomic model outperformed conventional MRI when predicting both the IDH wild type and IDH mutant and 1p/19q-codeleted subtypes of gliomas. • The multimodal radiomic model showed comparable performance to the combined model in the prediction of the three molecular subtypes. • Radiomic features from T1-weighted gadolinium contrast-enhanced and relative cerebral blood volume images played an important role in the prediction of molecular subtypes.

Diagnostic performance of perfusion-weighted imaging combined with serum MMP-2 and -9 levels in tumor recurrence after postoperative concomitant chemoradiotherapy of glioblastoma.

To evaluate diagnostic accuracy of dynamic susceptibility contrast- perfusion weighted imaging (DSC-PWI) combined with serum MMP-2 and -9 levels in differentiating recurrent glioblastoma (GBM). We enrolled a total of 220 GBM patients, including recurrent cases (n=150) and non-recurrent cases (n=70) after postoperative concomitant chemoradiotherapy. All patients performed preoperative and follow-up DSC-PWI, and two parameters [normalized cerebral blood volume (nCBV) and cerebral blood flow (nCBF)] were obtained. Preoperative serum levels of MMP-2 and MMP-9 were detected using ELISA. The diagnostic performance was evaluated by analyzing receiver operating characteristic (ROC) and area under the curve (AUC). At baseline, the recurrence patients had higher nCBF and nCBV than the non-recurrence patients, accompanying by the increased MMP-2 and MMP-9 levels in serum. Serum MMP-2 level were positively associated with MMP-9 in recurrent patients. In patients classified as recurrence, both MMP-9 and MMP-2 in serum had a significant correlation with nCBV and nCBF. A sensitivity and specificity of nCBF for recurrence vs. non-recurrence were 94.29% and 63.33%, respectively. nCBV also could provide high discrimination between recurrence and non-recurrence patients (sensitivity: 84.29%, specificity: 62.67%, AUC: 0.821). In ROC analyses, both MMP-2 and MMP-9 distinguished recurrence from non-recurrence with AUC values of 0.883 and 0.900, respectively. Finally, the combination of DSC-PWI parameters (nCBF and nCBV) and serum MMP-2 and -9 levels showed much better discrimination capacity between recurrence and non-recurrence patients with a sensitivity of 92.86%, specificity of 79.33% and AUC of 0.899. The combination of DSC-PWI parameters together with serum MMP-2 and -9 levels offered an attractive approach to noninvasively distinguish recurrence after postoperative radiotherapy of GBM.

Cerebral blood flow dependency on systemic arterial circulation in progressive multiple sclerosis

Background: Cerebrovascular reactivity (CVR) is a regulatory mechanism responsible for redistributing cerebral blood flow based on vasoactive stimuli and fluctuations in metabolic demand. Under normal conditions and proper functioning of the neurovascular unit, a direct association between cerebral arterial blood flow and systemic arterial blood flow (SABF) is not expected. Previous studies have suggested impairment of CVR in multiple sclerosis (MS).1 Moreover, lower cerebral perfusion in MS has been linked with greater neuronal pathology and worse disability outcomes.2,3&#x0D; Objectives: To determine the relationship between SABF and cerebral perfusion measures in MS patients.&#x0D; Мaterials and methods: Cerebral perfusion and SABF were assessed in 118 patients (75 clinically isolated syndrome (CIS)/relapsing-remitting MS and 43 progressive MS) through magnetic resonance imaging (MRI) examination with dynamic susceptibility contrast perfusion-weighted imaging (DSC-PWI) and Doppler ultrasound, respectively. Measures of mean transit time (MTT) and time-to-peak (TTP), measured in seconds, of the normal-appearing whole brain (NAWB) and gray matter (GM) were calculated. Blood flow through the bilateral common carotid and vertebral arteries (in mL/min) represents the SABF. Whole brain volume (WBV) and body mass index (BMI) were used as additional covariates.&#x0D; Results: Higher systolic blood pressure was associated with lower SABF (-0.256, p=0.006). In the total MS sample, higher SABF was associated with shorter MTT and TTP of the NAWB (r=-0.256, p=0.007 and r=-0.307, p=0.001) and GM (r=-0.239, p=0.012 and r=-0.3, p=0.001). The SABF and TTP associations were driven by the progressive multiple sclerosis (PMS) patients (r=-0.451, p=0.004 and r=-0.451, p=0.011). Only in PMS, SABF remained a significant predictor of NAWB (standardized β=-0.394, p=0.022) and GM TTP (standardized β=-0.351, p=0.037). MTT and TTP were significantly lower in patients within lower SABF quartiles when compared to the higher quartiles (age, sex, BMI and WBV-adjusted ANCOVA p&lt;0.025). (Figure 1) The differences in PWI-based measured remained significant after additional correction for the prevalence of hypertension. Although statistically non-significant, PMS patients within the lower SABF quartiles had a numerically greater disability (expanded disability status scale scores of 5.9 vs 5.0 for Q1 and Q4), (multiple sclerosis severity score of 5.8 vs 3.8), slower walking as measured by timed 25-foot walk test (23.9 s vs 15.8 s) and worse hand dexterity as measured by 9-hole peg test (50.8 s vs. 29.3 s).&#x0D; Conclusions: The direct relationship between systemic and cerebral blood flow seen in PMS patients may suggest a failure in cerebrovascular reactivity mechanisms and insufficient perfusion control. Cerebral blood flow in PMS may be increasingly dependent on the SABF.

Radiomics features of DSC-PWI in time dimension may provide a new chance to identify ischemic stroke.

Ischemic stroke has become a severe disease endangering human life. However, few studies have analyzed the radiomics features that are of great clinical significance for the diagnosis, treatment, and prognosis of patients with ischemic stroke. Due to sufficient cerebral blood flow information in dynamic susceptibility contrast perfusion-weighted imaging (DSC-PWI) images, this study aims to find the critical features hidden in DSC-PWI images to characterize hypoperfusion areas (HA) and normal areas (NA). This study retrospectively analyzed 80 DSC-PWI data of 56 patients with ischemic stroke from 2013 to 2016. For exploring features in HA and NA,13 feature sets (F method ) were obtained from different feature selection algorithms. Furthermore, these 13 F method were validated in identifying HA and NA and distinguishing the proportion of ischemic lesions in brain tissue. In identifying HA and NA, the composite score (CS) of the 13 F method ranged from 0.624 to 0.925. F Lasso in the 13 F method achieved the best performance with mAcc of 0.958, mPre of 0.96, mAuc of 0.982, mF1 of 0.959, and mRecall of 0.96. As to classifying the proportion of the ischemic region, the best CS was 0.786, with Acc of 0.888 and Pre of 0.863. The classification ability was relatively stable when the reference threshold (RT) was <0.25. Otherwise, when RT was >0.25, the performance will gradually decrease as its increases. These results showed that radiomics features extracted from the Lasso algorithms could accurately reflect cerebral blood flow changes and classify HA and NA. Besides, In the event of ischemic stroke, the ability of radiomics features to distinguish the proportion of ischemic areas needs to be improved. Further research should be conducted on feature engineering, model optimization, and the universality of the algorithms in the future.